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The three pillars of a better picking strategy

19 February 2018 / by Don White, VP Enterprise Solutions for Snapfulfil North America

In the on-demand economy, time is money. Thousands of orders pour into warehouses daily, and every minute an employee traverses a stadium-sized fulfillment center is another minute items aren’t being packed or shipped.

Warehouse managers often focus on improving their picking procedures as a solution for their inefficiency woes – a strategic move in a “less is more” environment. By cutting down the amount of time wasted on these tasks, managers can move more inventory through the warehouse without adding extra labor.


If you’re ready to rid yourself of picking pain points, you’ll need to gather data on the three “pillars” of picking: location, labor and tracking. Start by taking a high-level view of each pillar – and once you’ve identified your weaknesses, you’ll be better positioned to select the most effective solution.

Take a deeper look at how data collection plays a role in improving each pillar:

Location: Shortening the path from point A to point B

Perhaps a warehouse’s biggest picking pain point stems from inventory location. Barcoding solutions help warehouse managers keep track of the correct location for items, but that’s only one part of the equation. If an item is put away improperly, or a picker needs two items on opposite ends of the warehouse, labor could spend valuable time completing a single order.

Your sales data can be an invaluable tool in fixing stock location issues. Start by mapping the locations of your most popular SKUs; if they’re distant from your packing stations, consider moving them closer to prevent frequent runs across the warehouse. Then, look at which SKUs are most often purchased together. Moving these items closer to each other could significantly reduce labor travel time – and, as buying trends shift, the ability to alter inventory locations on the fly is critical for productivity.

Labor: Cover more ground efficiently

No matter how advanced your warehouse management system, it won’t carry out picking processes for you. Labor productivity rates are key in decreasing your picking costs. Fixing your inventory layout won’t always fix your productivity issues; you must ensure your staff is assigned well enough to handle an influx of orders.

Examining your productivity data will give you a better idea of how to address labor inefficiencies. Key in on your picked units per hour per picker, and then parse the data further: which areas of the warehouse are lagging? Is it an issue that can be fixed by reorganizing stock, or is it a specific group of workers who are moving slower? Consider offering extra training for employees or move more experienced staffers to the underperforming area. 

Tracking: Manual processes lead to mistakes

For even the smallest warehouses, relying on pencil and paper processes can slow down the picking process. When you record inventory without a digital backup, you’re more likely to make an error in data recording – which then must be rectified during the picking process. You’ll also have to maintain a detailed file of each hard copy, and even one missing document can throw a wrench in your procedures.

If you still manage inventory manually, spend a week tracking how much time you spend manually transferring data to your computer, and how many errors occur as the result of a tracking typo. Chances are, the monetary value of the time you save using warehouse management software to capture data will outweigh the solution cost.

Dig deeper into your data with WMS

Without an advanced warehouse management system, chances are you’re only able to take a high-level view of your data – you know how many items are shipped out in a day, but you may not know, on average, how quickly labor is picking items. Without deeper visibility into your pain points, it’s difficult to course correct.

Before you invest in a WMS, gather a list of problems you’ve found in your location, labor and tracking pillars by observing processes. Make sure that any software you implement addresses these problems. And once you begin tracking data more closely, you’ll find that less certainly is more.